LSNet: Learned Sampling Network for 3D Object Detection from Point Clouds

The3D object detection of LiDAR point cloud data has generated widespread discussion and implementation in recent years. In this paper, we concentrate on exploring the sampling method point-based 3D autonomous driving scenarios, a process which attempts to reduce expenditure by reaching sufficient accuracy using fewer selected points. FPS (farthest sampling), most used method, works poorly small size cases, and, limited massive points, some newly proposed methods deep learning are not suitable for scenarios. To address these issues, propose learned network (LSNet), single-stage containing an LS module that can sample important points through learning. This advanced approach with task-specific focus while also being differentiable. Additionally, is streamlined computational efficiency transferability replace more primitive other networks. issue high repetition rates sampled loss algorithm was developed. The validated KITTI dataset outperformed methods, such as F-FPS (FPS based feature distance). Finally, LSNet achieves acceptable only 128 shows promising results when number small, yielding up 60% improvement against competing eight